compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
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Updated
Jun 18, 2021 - Python
compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss
The Mulan Framework with Multi-Label Resampling Algorithms
The final project for the CE888: Data Science and Decision Making module (Spring Term) at the University of Essex
software vulnerability detection
Submission for HR Analytics Hackathon - AnalysticsVidya.
Dice loss for data-imbalanced NLP tasks
Deep Regression Tracking with Shrinkage Loss (ECCV 2018).
Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.
Customer Retention Analysis : Predict customer churn
The project is based on Indian and Southeast Asian market where mostly prepaid payment model is prevelant In this project we will use the usage-based chrun definition i.e. customers who have not done any usage either incoming or outgoing in terms of calls, internet etc. over a period of time. We focus only the High Value customers, as typically …
A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4
Classification of Body postures using different ML algorithms and comparing their performances.
Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.
ECG Arrhythmia Detection with ResNet and Transfer Learning
Applied undersampling and oversampling using SMOTE.
Predicting whether a client will subscribe for a term deposit after a bank marketing campaign
Detection of dermoscopic structures for melanoma diagonsis
This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.
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